Results 71 to 80 of about 6,652,811 (287)

Graph Representation Learning

ESANN 2023 proceesdings, 2023
AbstractGraph structure, which can represent objects and their relationships, is ubiquitous in big data including natural languages. Besides original text as a sequence of word tokens, massive additional information in NLP is in the graph structure, such as syntactic relations between words in a sentence, hyperlink relations between documents, and ...
Cheng Yang, Yankai Lin, Zhiyuan Liu, Maosong Sun   +3 more
openaire   +4 more sources

Additive Angular Margin Loss in Deep Graph Neural Network Classifier for Learning Graph Edit Distance

IEEE Access, 2020
The recent success of graph neural networks (GNNs) in the area of pattern recognition (PR) has increased the interest of researchers to use these frameworks in non-euclidean structures.
Nadeem Iqbal Kajla, Malik Muhammad Saad Missen, Muhammad Muzzamil Luqman, Mickael Coustaty, Arif Mehmood, Gyu Sang Choi   +5 more
doaj   +1 more source

Evaluation of a novel EHR sidecar application to display RA clinical outcomes during clinic visits: results of a stepped‐wedge cluster randomized pragmatic trial

Arthritis Care &Research, Accepted Article.
Objective We developed a novel EHR sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk, Jing Li, Cathy Nasrallah, Cherish Wilson, Alicia Hamblin, Christine Hariz, Cammie Young, Stephen Shiboski, Emma Kersey, Mary Nakamura, Judith F. Ashouri, Mehrdad Matloubian, Andrew J. Gross, Jennifer Barton, Beth Berrean, Jonathan Prugh, Jinoos Yazdany   +16 more
wiley   +1 more source

SCSU–GDO: Superpixel Collaborative Sparse Unmixing with Graph Differential Operator for Hyperspectral Imagery

Remote Sensing
In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in ...
Kaijun Yang, Zhixin Zhao, Qishen Yang, Ruyi Feng   +3 more
doaj   +1 more source

A Concept of a Digital and Traceable Manufacturing Documentation Based on Formalized Process Description Applied on Composite Aircraft Moveable

Advanced Engineering Materials, EarlyView.
The documentation of component manufacture has become an essential part of today's production processes, especially for the analysis and optimization of production or component design with regard to structural performance, economic efficiency, and sustainability.
Björn Denker, Dominik Delisle, Christian Krombholz, Martin Rädel, Frank Dressel   +4 more
wiley   +1 more source

Building Information Graphs (BIGs): remodeling building information for learning and applications

Data-Centric Engineering
Despite significant advances in Building Information Modeling (BIM) and increased adoption, numerous challenges remain. Discipline-specific BIM software tools with file storage have unresolved interoperability issues and do not capture or express ...
Zijian Wang, Rafael Sacks
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Lean-NET-Based Local Brain Connectome Analysis for Autism Spectrum Disorder Classification

Bioengineering
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and communication, along with atypical behavioral patterns.
Aoumria Chelef, Demet Yuksel Dal, Mahmut Ozturk, Mosab A. A. Yousif, Gokce Koc   +4 more
doaj   +1 more source

Review on graph learning for dimensionality reduction of hyperspectral image

Geo-spatial Information Science, 2020
Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction
Liangpei Zhang, Fulin Luo
doaj   +1 more source

GDLL: A Scalable and Share Nothing Architecture Based Distributed Graph Neural Networks Framework

IEEE Access, 2022
Deep learning has recently been shown to be effective in uncovering hidden patterns in non-Euclidean space, where data is represented as graphs with complex object relationships and interdependencies.
Duong Thi Thu Van, Muhammad Numan Khan, Tariq Habib Afridi, Irfan Ullah, Aftab Alam, Young-Koo Lee   +5 more
doaj   +1 more source